The objective of this paper is to analyze the effect on utility finances and consumer tariffs of implementing utility-funded cost-effective energy efficiency (EE) programs in India. We use the state of Delhi as a case study. A number of studies have demonstrated that end-use EE improvements in the Indian electricity sector has large potential for reducing power shortages, which would enhance the country{\textquoteright}s energy security and could play a crucial role in India{\textquoteright}s climate change mitigation plan. However, consumers face several barriers to adopting EE measures, including high initial cost, split incentives, and lack of information. In India, high initial cost is the most important barrier given the low income levels of the vast majority of electricity consumers and the country{\textquoteright}s relatively underdeveloped credit markets. This barrier can be effectively addressed through utility-funded EE programs.

Adoption of efficient end-use technologies is one of the key measures for reducing greenhouse gas (GHG) emissions. How to effectively analyze and manage the costs associated with GHG reductions becomes extremely important for the industry and policy makers around the world.

Energy-climate (EC) models are often used for analyzing the costs of reducing GHG emissions for various emission-reduction measures, because an accurate estimation of these costs is critical for identifying and choosing optimal emission reduction measures, and for developing related policy options to accelerate market adoption and technology implementation. However, accuracies of assessing of GHG-emission reduction costs by taking into account the adoption of energy efficiency technologies will depend on how well these end-use technologies are represented in integrated assessment models (IAM) and other energy-climate models.

In this report, we first conduct a brief review of different representations of end-use technologies (mitigation measures) in various energy-climate models, followed by problem statements, and a description of the basic concepts of quantifying the cost of conserved energy including integrating no-regrets options. According to IPCC (2001), no-regrets opportunities for GHG emissions reduction are the options whose benefits such as reduced energy costs and reduced emissions of local or regional pollutants equal or exceed their costs to society, excluding the benefits of avoided climate change. In this report, a no-regrets option is defined as a GHG reduction option (i.e., via energy efficiency measure) that is cost effective over the lifetime of the technology compared with a given energy price, without considering benefits of avoided climate change. There are two types of treatments of no-regrets options: 1) options that include other benefits, e.g., reduced operational and maintenance costs and productivity benefits; and 2) options that exclude other benefits. Although existence of no-regret options is not acknowledged by some economists, a number of cost-effective measures were identified in the U.S. iron and steel sector, regardless whether or not other benefits are included. There are many factors, including market barriers and knowledge gap, which contribute to slower adoption of such measures in the markets.

Based upon reviews of literature and technologies, we develop information on costs of mitigation measures and technological change. These serve as the basis for collating the data on energy savings and costs for their future use in integrated assessment models. In addition to descriptions of the iron and steel making processes, and the mitigation measures identified in this study, the report includes tabulated databases on costs of measure implementation, energy savings, carbon-emission reduction, and lifetimes.

Through characterizing energy-efficiency technology costs and improvement potentials, we have developed and presented energy cost curves for energy efficiency measures applicable to the U.S. iron and steel industry for the years 1994 and 2002. The cost curves can change significantly under various scenarios: the baseline year, discount rate, energy intensity, production, industry structure (e.g., integrated versus secondary steel making and number of plants), efficiency (or mitigation) measures, share of iron and steel production to which the individual measures can be applied, and inclusion of other non-energy benefits. Inclusion of other non-energy benefits from implementing mitigation measures can reduce the costs of conserved energy significantly. In addition, costs of conserved energy (CCE) for individual mitigation measures increase with the increases in discount rates, resulting in a general increase in total cost of mitigation measures for implementation and operation with a higher discount rate. As all the cost data (U.S. dollars) are obtained and presented as the currency values for the respective reference years (i.e., 1994, 2002), a direct comparison of costs (U.S. dollars), when desired, can be made by converting the existing reference-year data (i.e., 1994, 2002 in this study) to a preferred reference year (e.g., 2007). The conversions may be accomplished by multiplying the existing cost numbers represented in a reference year by an inflation index based on Gross Domestic Product (GDP) for the preferred year (BEA 2009).

The cost curve data on mitigation measures are available over time, which allows an estimation of technological change over a decade-long historical period. In this study, we compared the same set of mitigation measures for both 1994 and 2002. No additional mitigation measures for year 2002 were included due to unavailability of such data. Based upon the available data and cost curves, the rate of change in the savings potential at a given cost can be evaluated and be used to estimate future rates of change that can be the input for energy-climate models.

In 1994, integrated steel mills in the U.S. produced 55.4 Mt steel and secondary steel mills produced 35.9 Mt steel, for a total of 91.3Mt steel production in the United States (IISI 1994). Primary energy use for integrated steel making was 1,444 petajoules (PJ), over three times the energy use in secondary steel making, which was 426 PJ. The total carbon emissions from steel making related to energy use in 1994 were 34.3 MtC, with 78\% of these emissions from integrated steel making (26.9 MtC) and the rest (22\%) from the secondary steel making. In 2002, integrated steel mills in the U.S. produced 50.1 Mt steel and secondary steel mills produced 50.8 Mt steel, for a total of 100.9 Mt annual steel production. Primary energy use for integrated steel making was 1115 PJ, about twice of the energy use in secondary steel making, which was 519 PJ. The total carbon emissions from steel making related to energy use in 2002 was 30.6 MtC, with 71\% of these emissions from integrated steel making (21.9 MtC) and the rest (29\%) from the secondary steel making. We calculated that from 1994 to 2002 the steel production energy intensity has decreased by 15\% and 14\% for integrated steel and secondary steel, respectively indicating efficiency technology uptakes for both sectors over the period of time. In addition, the production shift from integrated steel to much less energy intensive secondary steel, in combination with the observed technology uptakes, resulted in an overall reduction in energy intensity by 21\% for the U.S. iron and steel industry from 1994 to 2002.

We estimated that the potential savings of final energy use resulting from applicable mitigations measures was 397 PJ in 1994 (287 PJ for integrated steel making, and 110 PJ for secondary steel making), and 304 PJ in 2002 (223 PJ for integrated steel making, and 81 PJ for secondary steel making). The potential annual energy savings corresponded to 25\% and 24\% of total annual final energy use in the U.S. iron and steel sector in 1994 and 2002, respectively.

We have identified a number of cost-effective mitigation measures in this study. Furthermore, inclusion of other benefits from implementing mitigation measures can reduce the costs of conserved energy significantly, making more measures cost-effective. Using the final energy price of US$2.59/GJ in 1994 and US$3.49/GJ in 2002, a number of measures are identified to be cost-effective in this study when including non-energy benefits. We estimated that the potential savings of final energy use resulting from the cost-effective mitigations measures was 251 PJ in 1994 (186 PJ for integrated steel making, and 65 PJ for secondary steel making), and 217 PJ in 2002 (144 PJ for integrated steel making, and 73 PJ for secondary steel making). Overall, implementing applicable cost-effective mitigation measures could result in potential final energy savings by 16\% and 17\% of the total annual final energy use in the U.S. iron and steel sector in 1994 and 2002, respectively.

We also estimated overall potentials in carbon-emission reductions due to mitigation measures for both years (1994 and 2002), respectively. In this study, we have developed and defined the concept of cost curves for carbon reduction associated with the mitigation measures. The potential reduction of carbon emissions resulting from the applicable mitigation measures was 6.1 million ton of carbon (MtC) in 1994 (3.9 MtC from integrated steel making, and 2.2 MtC from secondary steel making), and 5.7 MtC in 2002 (3.7 MtC from integrated steel making, and 2.0 MtC from secondary steel making), corresponding to 18\% and 19\% of annual energy-related carbon emissions in 1994 and 2002, respectively. Applying cost-effective measures would reduce carbon emissions by 4.7 MtC in 1994 (3.4 MtC from integrated steel making, and 1.3 MtC from secondary steel making), and 4.4 MtC in 2002 (2.7 MtC from integrated steel making, and 1.7 MtC from secondary steel making), corresponding to approximately 14\% of annual energy-related carbon emissions in each year.

We have also concluded that based upon the cost curves derived from available information on mitigation measures for both years, the rate of change in the energy-savings or carbon-reduction potential at a given cost can be evaluated and be used to estimate future rates of change for input in energy-climate models. Accuracies of such estimation of the rate change may be improved as more comprehensive information on characterizing the mitigation measures becomes available. Implementing existing cost effective measures can result in significant energy savings and carbon-emission reduction for both years relative to their technical potential in energy savings and carbon-emission reduction. In addition, total costs of conserved energy increase with the increases in discount rates. The outcomes from this research provide information on initial technology database that can be accessible to integrated assessment modeling groups seeking to enhance their empirical descriptions of technologies.

While many energy efficiency technologies have become cost-effective to mitigate long-term climate change, it is important and necessary to continue to incorporate new information on technology characteristics, and their evolution and response to energy and carbon price into various integrated assessment models to enhance empirical descriptions of the technologies, e.g., econometric models, service demand models, discrete choice models, or computational general equilibrium (CGE) models.

There appears to be a need to develop and refine sectoral algorithms and produce databases that can be used to match the needs of different integrated assessment modeling of climate policies. New algorithms should allow transformation of information on behavioral responses, technology costs, energy savings, other benefits, and policy costs into meaningful and functional data forms. Developing such algorithms may require customization and automation of database functions that would account for many variables. Furthermore, the desired data-model linking effort will require close interfaces between modelers and the developers of the cost-curve databases on energy efficiency measures. Future efforts should also include additional business sectors.

This study of residential and transport sectoral energy use in India is part of a larger effort at LBNL to provide analysis of energy use patterns at the level of sub-sectors and end uses for all sectors. There are two motivations for this effort. First, as the negative environmental impacts (both local and global) of energy consumption become more urgent, there is a need to evaluate current and future sources of energy-related effects at a greater level of accuracy and detail. Secondly, a disaggregated analysis is highly desirable in order to guide mitigation efforts, including policies towards increased efficiency.LBNL has a long history in the investigation of energy use patterns in developing countries, particularly in China. Most recently, these efforts have focused on end-use level analysis of historical and projected energy consumption in all Chinese energy sectors (Zhou, 2007). India seems poised to be the next emerging giant, in both economic and energetic terms. This report focusing on two key sectors will constitute one of the first in a series of steps on the details of recent trends in order to inform the development of effective policies to address the negative impacts of energy demand growth. This report looks at energy used at the end use level over the period 1990 to 2005 and develops a baseline scenario to 2020. End-use sector-level information regarding adoption of particular technologies was used as a key input in the bottom-up modeling approach.

This paper explores the feasibility of integrating energy efficiency program evaluation with the emerging need for the evaluation of programs from different "energy cultures" (demand response, renewable energy, and climate change). The paper reviews key features and information needs of the energy cultures and critically reviews the opportunities and challenges associated with integrating these with energy efficiency program evaluation. There is a need to integrate the different policy arenas where energy efficiency, demand response, and climate change programs are developed, and there are positive signs that this integration is starting to occur.

Assumptions regarding the magnitude and direction of energy-related technological change have long been recognized as critical determinants of the outputs and policy conclusions derived from integrated assessment models. Particularly in the case of developing countries, however, empirical analysis of technological change has lagged behind simulation modeling. This paper presents estimates of sectoral productivity trends and energy-augmenting technological change for several energy-intensive industries in India and South Korea, and, for comparison, the United States. The key findings are substantial heterogeneity among both industries and countries, and a number of cases of declining energy efficiency. The results are subject to certain technical qualifications both in regards to the methodology and to the direct comparison to integrated assessment parameterizations. Nevertheless, they highlight the importance of closer attention to the empirical basis for common modeling assumptions.

In this paper we discuss long-term least cost CO2 stabilization scenarios based on the SRES AIM A1B scenario in the context of an international burden-sharing regime. Starting from a stabilization target, regional emission caps are formulated dynamically on the basis of past emissions. With these regional caps, the cost-optimal supply fuel mix in the energy sector in the four SRES world regions is calculated, and lower bounds on the volume of traded carbon are estimated. The allocation scheme provides incentives for early mitigation action. We estimate additional regional costs incurred by the allocation scheme, and assess the sensitivity of results to changes in the concentration ceiling, discount rates, and start date for burden sharing.

Pooled data across several developing countries and the U. S. were used to estimate long- run substitution and price elasticities in a translog framework for the paper, iron and steel, and aggregate manufacturing industries. While the quality of the estimates varies across the several industry-specific models, the results suggest higher values for these elasticities than appear commonly used in integrated assessment models. Estimates of own-price elasticities of energy range from -0.80 to -1.76 and are comparable to estimates from previous econometric studies in the context of developed countries (-0.77 to -0.87). Substitution elasticities show wider variation across countries and industries. For energy and capital they range from -1.96 to 9.80, for labor and energy from 2.61 to 7.11, and for energy and material from -0.26 to 2.07.

Conservation supply curves are a common tool in economic analysis. As such, they provide an important opportunity to include a non-linear representation of technology and technological change in economy-wide models. Because supply curves are closely related to production isoquants, we explore the possibility of using bottom-up technology assessments to inform top-down representations of energy models of the U.S. economy. Based on a recent report by LBNL and ACEEE on emerging industrial technologies within the United States, we have constructed a supply curve for 54 such technologies for the year 2015. Each of the selected technologies has been assessed with respect to energy efficiency characteristics, likely energy savings by 2015, economics, and environmental performance, as well as needs for further development or implementation of the technology. The technical potential for primary energy savings of the 54 identified technologies is equal to 3.54 Quads, or 8.4 percent of the assumed2015 industrial energy consumption. Based on the supply curve, assuming a discount rate of 15 percent and 2015 prices as forecasted in the Annual Energy Outlook2002, we estimate the economic potential to be 2.66 Quads {\textemdash} or 6.3 percent of the assumed forecast consumption for 2015. In addition, we further estimate how much these industrial technologies might contribute to standard reference case projections, and how much additional energy savings might be available assuming a different mix of policies and incentives. Finally, we review the prospects for integrating the findings of this and similar studies into standard economic models. Although further work needs to be completed to provide the necessary link between supply curves and production isoquants, it is hoped that this link will be a useful starting point for discussion with developers of energy-economic models.

Historically, most energy models were reasonably equipped to assess the impact of a subsidy or change in taxation, but are often insufficient to assess the impact of more innovative policy instruments. We evaluate the models used to assess future energy use, focusing on industrial energy use. We explore approaches to engineering-economic analysis that could help improve the realism and policy relevance of engineering-economic modeling frameworks. We also explore solutions to strengthen the policy usefulness of engineering economic analysis that can be built from a framework of multi-disciplinary cooperation. We focus on the so-called {\textquoteright}engineering-economic{\textquoteright} (or {\textquoteright}bottom-up{\textquoteright}) models, as they include the amount of detail that is commonly needed to model policy scenarios. We identify research priorities for the modeling framework, technology representation in models, policy evaluation, and modeling of decision-making behavior.

Energy is a fundamental component of myriad services and benefits to humanity in pursuit of a healthy and productive life, including production of food and other essential goods; provision of buildings for housing, education, health care, and commerce; and provision of transportation for goods and people. However, production and consumption of fossil fuel-based energy, which accounts for approximately 85\% of total energy consumption in the United States can also result in scarring or pollution of the environment during extraction of the fuels and contributes to local air pollution and smog formation, regional acid rain production, and global warming as the fuels are burned. Further, continued large-scale consumption of nonrenewable energy sources will eventually lead to depletion of these resources and future generations will need to rely on alternative sources of energy. Thus, the significant characteristics of sustainable development for the energy sector include more efficient use of nonrenewable fossil fuel-based energy resources, development of technologies to significantly reduce local and global pollutants from fossil fuels, and increased development and use of renewable energy resources.

This Article looks at the characteristics of sustainable development vis-a-vis energy consumption and production, reviews the laws and policies enacted in the United States that could contribute to more sustainable energy consumption and production, and evaluates actual achievements in three areas that measure sustainability of energy consumption and production. We find that although there are many guiding principles relevant to energy sustainability and there have been numerous energy-related laws and policies enacted both prior to and after the 1992 Rio Declaration on Environment and Development, growth in fossil fuel-based energy use as well as in energy-related greenhouse gas (GHG) emissions was more rapid{\textemdash}and thus less sustainable{\textemdash}in the eight years after 1992 than in the two decades prior to Rio. We recommend a comprehensive package of policies and measures that includes carbon fees, increased research and development (R\&D), expanded efficiency standards, building codes, tax credits for energy efficiency and renewable energy investments, expanded government procurement programs, negotiated agreements with industries to improve energy intensity of manufacturing, increased information dissemination, promotion of combined heat and power, increased fuel efficiency standards for vehicles, etc. A recent study that analyzed the combined impact of this comprehensive package found that, if very aggressive policies were introduced in all sectors and significant research and development breakthroughs occurred in the transportation sector, it might be possible to reduce energy-related carbon dioxide (CO2) emissions to 1990 levels by 2020.

During the period 1973 to 1985, the U.S. economy saved energy in virtually every sector. Much of this period of energy saving was also marked by a significant drop in the ratio of energy use to GDP. However, since 1985 there has been a slowdown in the rate of energy saving, as key energy intensities (space heating, automobile driving, etc.) have declined less rapidly since 1985 than before. This paper examines delivered (or final) energy consumption trends from the early 1970s to 1994 and provides a framework for measuring key changes that affect U.S. energy use. Starting with estimates of outputs or activity levels for thirty major energy end uses, and energy intensities of each end use, we use the Adaptive Weighted Divisia decomposition to measure the impact of changes in the structure of the U.S. economy. In contrast to many similar decomposition studies, we define measures of structural changes for both households and branches of transportation. We find that between 1973 and 1985, lower energy intensities (corrected to average winter heating demand) reduced U.S. energy uses by about 1.7\% per year, while structural changes reduced energy uses by 0.4\% per year. After 1985, when oil prices declined markedly, intensities fell by only 0.8\% per year and structural changes actually increased energy use by 0.4\% per year. In the 1990s energy intensities in some industries have even edged upward. Changes in the ratio of energy to GDP (E/GDP) are affected both by intensities and the changes in the demand for energy services relative to GDP. During the first period, from 1973 to 1985, GDP increased faster than the growth in key structural and activity parameters that determine demand for energy services (such as home area, appliance ownership, and motor vehicle use) by 1.5\% per year. From 1985 to 1994 the difference dropped to less than 0.3\% per year, largely due to the reversal of structural trends. Thus, the sharp fall in the rate of decline in E/GDP from -3.1\% to -1.1\% per year was due almost as much to structural changes as it was to the slowdown in energy intensity reduction. The analysis presented here shows why the E/GDP is an increasingly unreliable yardstick for making measurements of how the energy-economy relationship is changing: effects not related to energy efficiency per se may have roughly the same impact on that ratio as energy saving itself. Since these effects have different causes, and potentially different impacts over the long run, looking at them in the aggregate by considering only the ratio of energy use to GDP is misleading.

In previous work (Schipper, Unander \& Lilliu 1999), we summarized a new method for comparing energy use and carbon emissions among various countries. We call this the "Mine/Yours" comparison. In this paper, we provide details of the comparisons methodology, and carry out the comparison on a number of IEA countries. We calculate the average energy intensities I for a sample of countries ("yours") and multiply them by structural parameters S for a particular country ("mine"). Comparing the results with the actual energy use of the country in question gives us an estimate of how much energy that country would use with average intensities but with its own structural conditions. The converse can be calculated as well, that is, average structure and own intensities. Emissions can be introduced through the F (fuel mix) term. These calculations show where differences in the components of emissions lead to large gaps among countries, and where those differences are not important. We show which components cause the largest variance in emissions by sector. In households, home size, average winter climate, and energy intensity appear to be the most important differentiating characteristics for space heating. For other residential energy uses the mix of fuels used to generate electricity (utility mix) is most important. Because some of the differences are "built in" {\textemdash} geography, climate, natural resources endowment {\textemdash} we conclude by questioning whether uniform emissions reductions targets make sense. Indeed, the "Mine/Yours" tool provides a valuable guide to important ways in which emissions may or may not be flexible.

Energy is used in buildings to provide a variety of services such as lighting, space heating and cooling, refrigeration, and electricity for electronics and other equipment. In the U.S., building energy consumption accounts for nearly one-third of total primary energy consumption and related greenhouse gas emissions. The cost of delivering all energy services in buildings (such as cold food, lighted offices, and warm homes) will be over $220 billion in 1997.

Our analysis shows that substantial reductions in future greenhouse gas emissions can be realized through the use of more energy-efficient technologies that save society money. In addition, these technologies often supply other benefits beyond energy, carbon, and dollar savings, including the following:

improved indoor environment, comfort, health, and safety,

reduced noise,

improved process control, and

increased amenity or convenience (Mills and Rosenfeld 1994).

These indirect benefits, while difficult to quantify in economic terms, can be even more important than the energy cost savings, particularly when they improve the comfort of homeowners or the productivity of workers.

This chapter describes our detailed assessment of the achievable cost-effective potential for reducing carbon dioxide emissions in 2010. We calculate carbon, energy, and dollar savings associated with adoption of more energy-efficient technologies. In addition, this chapter qualitatively describes the role of research and development (R\&D) in providing a stream of advanced building technologies and practices after 2010 that will enable continued reduction in energy use and greenhouse gas emissions.

All costs in this chapter are reported in 1995 U.S. dollars (1995$). Carbon dioxide emissions are reported in terms of their carbon equivalent. To convert carbon dioxide units at full molecular weight into carbon units, divide by 44/12 or 3.67. For further information on emissions data, see EIA (1995).